Ghazal Mirabnahrazam1, Da Ma2,1, Sieun Lee1,3, Karteek Popuri1, Hyunwoo Lee4, Jiguo Cao5, Lei Wang6, James E Galvin7, Mirza Faisal Beg1. 1. School of Engineering, Simon Fraser University, Burnaby, BC, Canada. 2. School of Medicine, Wake Forest University, Winston-Salem, NC, USA. 3. Mental Health & Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom. 4. Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, BC, Canada. 5. Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, BC, Canada. 6. Psychiatry and Behavioral Health, Ohio State University Wexner Medical Center, Columbus, OH, USA. 7. Comprehensive Center for Brain Health, Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, USA.
Abstract
BACKGROUND: The increasing availability of databases containing both magnetic resonance imaging (MRI) and genetic data allows researchers to utilize multimodal data to better understand the characteristics of dementia of Alzheimer's type (DAT). OBJECTIVE: The goal of this study was to develop and analyze novel biomarkers that can help predict the development and progression of DAT. METHODS: We used feature selection and ensemble learning classifier to develop an image/genotype-based DAT score that represents a subject's likelihood of developing DAT in the future. Three feature types were used: MRI only, genetic only, and combined multimodal data. We used a novel data stratification method to better represent different stages of DAT. Using a pre-defined 0.5 threshold on DAT scores, we predicted whether a subject would develop DAT in the future. RESULTS: Our results on Alzheimer's Disease Neuroimaging Initiative (ADNI) database showed that dementia scores using genetic data could better predict future DAT progression for currently normal control subjects (Accuracy = 0.857) compared to MRI (Accuracy = 0.143), while MRI can better characterize subjects with stable mild cognitive impairment (Accuracy = 0.614) compared to genetics (Accuracy = 0.356). Combining MRI and genetic data showed improved classification performance in the remaining stratified groups. CONCLUSION: MRI and genetic data can contribute to DAT prediction in different ways. MRI data reflects anatomical changes in the brain, while genetic data can detect the risk of DAT progression prior to the symptomatic onset. Combining information from multimodal data appropriately can improve prediction performance.
BACKGROUND: The increasing availability of databases containing both magnetic resonance imaging (MRI) and genetic data allows researchers to utilize multimodal data to better understand the characteristics of dementia of Alzheimer's type (DAT). OBJECTIVE: The goal of this study was to develop and analyze novel biomarkers that can help predict the development and progression of DAT. METHODS: We used feature selection and ensemble learning classifier to develop an image/genotype-based DAT score that represents a subject's likelihood of developing DAT in the future. Three feature types were used: MRI only, genetic only, and combined multimodal data. We used a novel data stratification method to better represent different stages of DAT. Using a pre-defined 0.5 threshold on DAT scores, we predicted whether a subject would develop DAT in the future. RESULTS: Our results on Alzheimer's Disease Neuroimaging Initiative (ADNI) database showed that dementia scores using genetic data could better predict future DAT progression for currently normal control subjects (Accuracy = 0.857) compared to MRI (Accuracy = 0.143), while MRI can better characterize subjects with stable mild cognitive impairment (Accuracy = 0.614) compared to genetics (Accuracy = 0.356). Combining MRI and genetic data showed improved classification performance in the remaining stratified groups. CONCLUSION: MRI and genetic data can contribute to DAT prediction in different ways. MRI data reflects anatomical changes in the brain, while genetic data can detect the risk of DAT progression prior to the symptomatic onset. Combining information from multimodal data appropriately can improve prediction performance.
Entities:
Keywords:
Alzheimer’s disease; biomarker; early detection; machine learning; magnetic resonance imaging; risk scores; single nucleotide polymorphismzzm321990
Authors: Susanne G Mueller; Michael W Weiner; Leon J Thal; Ronald C Petersen; Clifford R Jack; William Jagust; John Q Trojanowski; Arthur W Toga; Laurel Beckett Journal: Alzheimers Dement Date: 2005-07 Impact factor: 21.566
Authors: Christopher C Chang; Carson C Chow; Laurent Cam Tellier; Shashaank Vattikuti; Shaun M Purcell; James J Lee Journal: Gigascience Date: 2015-02-25 Impact factor: 6.524